This project compares Report Data with Real Data by comparing the Health Choice Value Based Purchasing Quality Roster (VBPQR)1 for Antidepressant Medication Management (AMM) with Health Choice adjudicated claims. This project is similar in many ways to the VBP Validation Model for Follow Up after Hospitalization (FUH)2.
The guidelines for antidepressant medication are far more nuanced3. Nonetheless we have endeavored to define the eligible population using claims that have been processed and paid by Health Choice, and then measuring our Real Data against the VBP Quality Report Data.
The results were surprising! Our research into eligible adjudicated claims identified a similar number of cases to the VBP Quality Roster; however, only 16% of the VBP QR members were validated against adjudicated claims.
We hope you enjoy reading this data story. We are always looking for new collaborators, so please reach out and share your ideas.
Sincerely,
Ryan
The Alliance NCQA HEDIS My2023 Eligibility Model for AMM
The thing about measuring VBP
So, there we were in the first months of the Alliance ACO business, and EVERY provider was concerned that the Value Based Purchasing Quality Roster was inaccurate. Our providers were concerned that their performance scores were underrated, not giving them the credit they deserved.
The Alliance wants its providers to be their best selves and to be recognized. Lack of confidence in the scoring mechanism crowds out enthusiasm and damages commitment to improvement.
So, in January 2023 we developed a validation model for the NCQA measure for Follow Up after Hospitalization (FUH).
The VBP Validation Model FUH7 (2023), Key Takeaways:
Created a validated data model that can be used to identify eligible member events for FUH7
Core logic can be used to develop models for other HEDIS/NCQA measures
Research affirmed providers were underrated for the 2022 measurement year
Providers deserved a higher score by about 4 percentage points
So what’s next?
That brings us to the new measurement year, January 2023 to December 2023. In this project the VBP Validation Model is going to be applied to the Antidepressant Medication Management (AMM) HEDIS My20234 performance measure.
This model compares Report Data from the VBP Quality Roster with Real Data from adjudicated claims, or claims that have been processed and paid.
The model will answer the following questions:
Do the results of the Value Based Purchasing Quality Roster (VBPQR) accurately reflect actual adjudicated claims?
Are the performance scores of Alliance Providers accurate and reliable?
The business objectives are twofold:
Build an eligibility model for the AMM eligible population.
Validate the VBP Quality Report using adjudicated claims for eligible cases.
Why invest in this research?
Inaccurate measurement of VBP performance leads to an invalid assessment of the Alliance Providers’ delivery of services to their patients.
Alliance providers were underrated on Follow Up after Hospitalization (FUH7) by at least 4 percentage points for the 2022 measurement year5. With the constantly increasing emphasis on payment for quality of care, even small differences in measurement impact overall performance ratings.
Alliance Provider performance scores were underated (red blocks) by at least 4 percentage points in 2022
Underrating provider performance frustrates leadership and dampens the spirits of the clinical teams as they do this very difficult work.
Accurate measurement offers a better opportunity to identify patients in need, maximize performance incentive, and support provider morale.
Setting up the experiment
This manuscript is written using Brisk-DM. Brisk-DM is a structure for doing data science that is based on CRISP-DM6, but tailored for executives and business leads. Brisk-DM uses an approachable communication style that is easily understandable to professionals in the workplace.
The current state of things
At the time of this study, The Alliance has assessed the first 3 months of VBP Quality Rosters for the 2023 measurement year. Our application of the VBP Validation Model for FUH7 in June 2023 revealed a similar pattern to the 2022 measurement year where we found that performance scores for FUH were underrated7.
The results for FUH are under further investigation while we develop this model for AMM. The recurrence of this issue invites some concern. Learning from last year’s results, we will carefully monitor FUH through the data, and track performance throughout the year, reporting our findings in real time.
Hablemos de dato (Let’s talk about the data)
This data model is simple in concept, but complex in design. The model compares Report Data from the HC VBP Quality Roster with Real Data from Health Choice claims.
What are the data sources?
VBP Quality Roster
Adjudicated Claims
Health Choice receives this data from third party vendor, Cotivity.
A dataset of adjudicated claims queried directly from the HCA data warehouse.
An excel workbook containing a roster of the members deemed eligible for VBP HEDIS NCQA measures including compliance status.
Records are gathered from the Claims and PBM databases, respectively.
The Alliance receives individual rosters for each Alliance Provider.
Claims are extracted for all eligible service codes for the measurement year.
Is the data we use reliable?
VBP Quality Roster
Adjudicated Claims
The outstanding question with the VBPQR is if the underlying data from the third party vendor, Cotivity, is accurate.
Claims data is of the highest quality as it is compiled and reviewed extensively by Health Choice for its own business purposes.
The report quality itself is very high as it is compiled by Health Choice Business Intelligence staff.
The quality of this data is also reviewed by state and federal regulation entities, like AZ Health Care Cost Containment System (AHCCCS).
The underlying data is what is under investigation.
The AMM Eligibility Model
We now embark upon building a dataset of eligible claims for the AMM measure. This process will be significantly more complicated than our previous model for FUH. The description of an eligible member event is very nuanced for AMM.
An eligible member event is defined as follows:
The individual is an adult, aged 18 or older, and
A prescription was filled for an eligible antidepressant medication within the “Intake Period” (Index Prescription Start Date (IPSD)), and
No other prescriptions were filled for eligible antidepressant medications within 105 days earlier than the IPSD (Test for Negative Medication History (NMH)), and
The individual had a service for at least one of the eligible Major Depressive Disorders (Test for Major Depressive Disorder (MDDx)) within 60 days (-60 days before, or +60 days after), and
The individual had continuous enrollment with Health Choice (Test for Continuous Enrollment (CE)) for
105 days before the IPSD, and
231 days after the IPSD
Check out the visualization of the AMM Eligibility Model below. There are many components of this model with overlapping time frames. We will describe these steps in further detail in the next section.
AMM Eligibility Model, NCQA HEDIS My2023
You may also think of the criteria as a tumbling bike lock. All four criteria must line up, in the right order, for the lock to open. Once unlocked, the resulting dataset es El Dato Verdad, the Real Data.
AMM Eligibility Model, Primary criteria: 1. Index Prescription Start Date (IPSD) 2. Negative Medication History (NMH) 3. Major Depressive Diagnosis (MDDx) 4. Continuous Enrollment (CE)
The first criteria that must be met is the Index Prescription Start Date (IPSD). This is the date that an individual filled a prescription for one of the HEDIS My 2023 eligible antidepressant medications.
The IPSD is the anchor point for the rest of the eligibility measures. An individual must have an eligible IPSD in order to be assessed against the other tests for this measure.
The member must have an eligible prescription fill for an eligible antidepressant medication between the eligible dates indicated in the visualization above.
In order to assess IPSD we did the following:
The 12 month Intake Period begins on May 1 of the year prior to the Measurement Year
The Measurement Year is: 01-01-2023 to 12-31-2023
The Intake Period is: 05-01-2022 to 04-30-2023
We extract claims from the PBM database at the Health Choice data warehouse for the given My2023 eligible medications dispensed within the Intake Period.
The NDC to GPI crosswalk must be used to determine eligible medications. The code to create the crosswalk is in the Appendix.
The minimum date of the Test for Negative Medication History must also be included in the PBM Claims query. The test for NMH is outlined below.
The Negative Medication History range is: 01-16-222 to 01-15-2023
The prescription fill date in this range is called the Index Prescription Start Date (IPSD)
Extract claims data from HC data warehouse
Code
# DeclareDateRangefor NMH and IPSD rangeDeclare @start asdate='01-16-2022'Declare @end asdate='04-30-2023'# Select variablesSELECT pbm.clientID,id.PrimaryId, pbm.AsOfDate, pbm.dtefilled, pbm.GpiNumber, pbm.GPIClassification, pbm.prodname, pbm.genericnme, pbm.LabelName, pbm.GroupId, pbm.preslstnme, pbm.pbmrxclaimnbr, pbm.claimsts, pbm.AmtPaidFinal, pbm.productid, pbm.decimalqty, pbm.dayssupply, pbm.Gender, pbm.birthdte, pbm.mbrage# Identify data sourcesFROM PBM.dbo.HCICPharmacyClaimSummary pbmLEFTOUTERJOIN GlobalMembers.dbo.ClientIdPlus idON pbm.clientID =id.AzAhcccsId# Set conditions WHERE pbm.dtefilled BETWEEN @start AND @endAND pbm.mbrage >=18AND pbm.GpiNumber IN (concatenated_values_GPI)
Conduct IPSD test
Code
# Import the raw claimsPBMClaims <-read.csv("./data/DataRaw_PBMClaims_2023-05-31.csv") %>%mutate(ProjectId_IPSD =sample(1000000000:9999999999, nrow(.)))# Convert numeric variable to datePBMClaims$dtefilled <-as.Date(as.character(PBMClaims$dtefilled), format ="%Y%m%d")# Convert character variable to datePBMClaims$AsOfDate <-as.Date(PBMClaims$AsOfDate, format ="%m/%d/%Y")# write.csv(PBMClaims, "./data/output/PBMClaims.csv")# Copy data to a new name for the testingPBMClaims_IPSDTest <- PBMClaims# Convert the 'dtefilled' column to Date type if it's not already in the correct formatPBMClaims_IPSDTest$dtefilled <-as.Date(PBMClaims_IPSDTest$dtefilled)# Define the start and end dates for IPSDTestResultip_start_date <-as.Date("2022-05-01")ip_end_date <-as.Date("2023-04-30")# Create the IPSDTestResult column based on the date conditionsPBMClaims_IPSDTest$IPSDTestResult <-ifelse( PBMClaims_IPSDTest$dtefilled >= ip_start_date & PBMClaims_IPSDTest$dtefilled <= ip_end_date, "Valid IPSD", "No Valid IPSD")# write.csv(PBMClaims_IPSDTest, "./data/output/PBMClaims_IPSDTest.csv")
The provided code is a combination of SQL and R code for data processing and analysis. Here’s a layman’s summary:
The SQL code retrieves data from a database table called “PBM.dbo.HCICPharmacyClaimSummary” and joins it with the “GlobalMembers.dbo.ClientIdPlus” table using the client ID. It selects various columns related to pharmacy claim details within a specified date range, and filters for members aged 18 or older and specific GPI numbers.
The R code imports raw claims data from a CSV file, converts the date columns to the correct format, and saves the processed data as a new CSV file.
The R code then creates a copy of the claims data and adds a new column called “IPSDTestResult” based on date conditions. If the “dtefilled” date falls within a specific range, the result is set to “TRUE”; otherwise, it is set to “FALSE”. The modified data is saved as another CSV file.
The overall purpose of the code is to process and analyze pharmacy claims data, specifically focusing on members who meet certain age and GPI number criteria, and identifying if their claims fall within a specified date range for an IPSD test. (ChatGPT, personal communication, July, 2023).
The next stop on the road is to confirm that the individual had a service for at least one of the eligible Major Depressive Disorders (MDDx) within 60 days (-60 days before, or +60 days after) of the IPSD.
A diagnosis of Major Depressive Disorder is identified by cross referencing members with an eligible IPSD and NMH (using PBM records), against behavioral health claims records for MDD. Individual identifying data is used to join PBM records with claims records, and make the comparison.
Behavioral health claims are extracted from the HCA data warehouse for all cases where there was an eligible Major Depressive Disorder Diagnosis.
Conduct the MDDx Test by comparing each of the cases identified by PBMClaims with any of the MDDx claims.
The result is a big data solution consisting of over 1.7 million possible combinations. We now have grown our dataset to include an assessment for eligible behavioral health services.
Import the HEDIS My2023 value set for eligible Major Depressive Diagnoses
Code
# Set some things upd for the SQL Query# Import the Value Set ListValueSetListMy2023 <-read.csv("./data/ValueSetListMy2023.csv")# Create a list of the diagnosis codesMDDxList <- ValueSetListMy2023$Code# Concatenate for adding to sqlconcatenated_values_MDDx <-paste0("(", ValueSetListMy2023$Code, ")", collapse =", ")
Query the Health Choice data warehouse for eligible MDDx claims
Code
-- Declare start and end variablesDECLARE @start DATE='2022-03-22';DECLARE @end DATE='2023-10-30';-- Check if the temporary table exists and drop it if it doesIF OBJECT_ID('tempdb..#ValueSetListMy2023') ISNOTNULLDROPTABLE #ValueSetListMy2023;-- Create a temporary tableCREATETABLE #ValueSetListMy2023 (Code VARCHAR(100) COLLATE SQL_Latin1_General_CP1_CI_AS);-- Insert values into the temporary tableINSERTINTO #ValueSetListMy2023 (Code)VALUES ('101'), ('100'), ('207'), ('116'), ('126'), ('136'), ('146'), ('156'), ('110'), ('120'), ('130'), ('140'), ('150'), ('160'), ('170'), ('190'), ('200'), ('210'), ('1000'), ('213'), ('214'), ('206'), ('202'), ('111'), ('121'), ('131'), ('141'), ('151'), ('211'), ('171'), ('172'), ('173'), ('174'), ('122'), ('132'), ('142'), ('152'), ('112'), ('117'), ('127'), ('137'), ('147'), ('157'), ('119'), ('129'), ('139'), ('149'), ('159'), ('169'), ('219'), ('209'), ('179'), ('199'), ('113'), ('123'), ('133'), ('143'), ('153'), ('203'), ('114'), ('124'), ('134'), ('144'), ('154'), ('204'), ('212'), ('118'), ('128'), ('138'), ('148'), ('158'), ('1002'), ('1001'), ('167'), ('164'), ('191'), ('192'), ('193'), ('194'), ('201'), ('208'), ('F32.0'), ('F32.1'), ('F32.2'), ('F32.3'), ('F32.4'), ('F32.9'), ('F33.0'), ('F33.1'), ('F33.2'), ('F33.3'), ('F33.41'), ('F33.9'), ('14183003'), ('2618002'), ('726772006'), ('320751009'), ('36923009'), ('370143000'), ('1.08111E+16'), ('1.08112E+16'), ('42925002'), ('69392006'), ('63778009'), ('25922000'), ('87512008'), ('79298009'), ('1.6266E+16'), ('40379007'), ('720455008'), ('720454007'), ('720451004'), ('832007'), ('15639000'), ('1.62668E+16'), ('18818009'), ('719592004'), ('720453001'), ('720452006'), ('66344007'), ('38694004'), ('39809009'), ('319768000'), ('71336009'), ('268621008'), ('191610000'), ('191611001'), ('191613003'), ('1.62646E+16'), ('1.62649E+16'), ('1.62648E+16'), ('450714000'), ('73867007'), ('33736005'), ('60099002'), ('75084000'), ('2.51E+11'), ('430852001'), ('77911002'), ('20250007'), ('76441001'), ('1.6267E+16'), ('2.81E+11'), ('28475009'), ('33078009'), ('15193003'), ('36474008'), ('191604000');-- Query to retrieve data from the claims.dbo.shcavos tableSELECTDISTINCT shcavos.primaryID, id.BCBSMedicaidId AS MemberID, shcavos.begDate, shcavos.PrimaryDiagnosis, shcavos.Dx1, shcavos.Dx2, shcavos.Dx3, shcavos.Dx4, shcavos.Dx5, shcavos.Dx6, shcavos.Dx7, shcavos.Dx8, shcavos.Dx9, shcavos.Dx10, shcavos.Dx11, shcavos.Dx12,CASEWHEN v.Code ISNOTNULLTHEN'True'ELSE'False'ENDAS MatchFound-- Set data sourceFROM claims.dbo.shcavos AS shcavosLEFTJOIN GlobalMembers.dbo.ClientIdPlus idON shcavos.primaryID =id.primaryID-- Go through all of the Dx and look for eligible Dx codesLEFTJOIN #ValueSetListMy2023 AS v ON shcavos.PrimaryDiagnosis COLLATE SQL_Latin1_General_CP1_CI_AS = v.CodeOR shcavos.Dx1 COLLATE SQL_Latin1_General_CP1_CI_AS = v.CodeOR shcavos.Dx2 COLLATE SQL_Latin1_General_CP1_CI_AS = v.CodeOR shcavos.Dx3 COLLATE SQL_Latin1_General_CP1_CI_AS = v.CodeOR shcavos.Dx4 COLLATE SQL_Latin1_General_CP1_CI_AS = v.CodeOR shcavos.Dx5 COLLATE SQL_Latin1_General_CP1_CI_AS = v.CodeOR shcavos.Dx6 COLLATE SQL_Latin1_General_CP1_CI_AS = v.CodeOR shcavos.Dx7 COLLATE SQL_Latin1_General_CP1_CI_AS = v.CodeOR shcavos.Dx8 COLLATE SQL_Latin1_General_CP1_CI_AS = v.CodeOR shcavos.Dx9 COLLATE SQL_Latin1_General_CP1_CI_AS = v.CodeOR shcavos.Dx10 COLLATE SQL_Latin1_General_CP1_CI_AS = v.CodeOR shcavos.Dx11 COLLATE SQL_Latin1_General_CP1_CI_AS = v.CodeOR shcavos.Dx12 COLLATE SQL_Latin1_General_CP1_CI_AS = v.Code-- Set dateWHERE shcavos.begDate BETWEEN @start AND @end-- Return valueANDCASEWHEN v.Code ISNOTNULLTHEN'True'ELSE'False'END='True';# Save the result as MDDxClaims_dateslug.csv
Load data from the claims query results into the data model
Code
# Import the MDDxClaims from excelMDDxClaims <-read_csv("./data/MDDxClaims_20230602.csv")# write.csv(MDDxClaims, "./data/output/MDDxClaims.csv")
Conduct the MDDx Test
Code
# Step 1: Load the MDDxClaims dataMDDxClaimsTest <- MDDxClaims %>%rename(PrimaryId = primaryID)# Step 2: Add a new column "ProjectId_MDD" with random 10-digit numbersMDDxClaimsTest <- MDDxClaimsTest %>%mutate(ProjectId_MDD =sample(1000000000:9999999999, nrow(.))) |>filter(!is.na(begDate)) |>rename("MDDxDate"= begDate)# Step 2a: Shed a little extra weightMDDxClaimsTest <- MDDxClaimsTest %>%select(PrimaryId, MemberID, ProjectId_MDD, MDDxDate, PrimaryDiagnosis)# Step 3: Back to the IPSDTest data# Filter out cases with "No Valid IPSD"filtered_table <- PBMClaims_IPSDTest %>%filter(IPSDTestResult =="Valid IPSD")# Rename PrimaryId for consistency# filtered_table <- filtered_table |> # rename(PrimaryId = PrimaryId.x)# Step 4: Merge PBMClaims_NMHTest and MDDxClaims on PrimaryId, keeping all rows and columnsPBMClaims_MDDxMerge <-merge(x = filtered_table, y = MDDxClaimsTest, by ="PrimaryId", all.x =TRUE)# Step 4 Rogue: format as datePBMClaims_MDDxMerge <- PBMClaims_MDDxMerge %>%rename(IPSDate = dtefilled)# Step 4 Rogue: format as datePBMClaims_MDDxMerge <- PBMClaims_MDDxMerge %>%mutate(IPSDate =as.Date(IPSDate),MDDxDate =as.Date(MDDxDate))# Step 5: Determine the number of days between IPSDate and MDDxTestDate as an absolute valuePBMClaims_MDDxTest <- PBMClaims_MDDxMerge %>%mutate(days_between_MDDx =abs(as.numeric(IPSDate - MDDxDate)))# Step 6: Determine "Positive MDDx" or "No MDDx" based on days_betweenPBMClaims_MDDxTest <- PBMClaims_MDDxTest %>%mutate(MDDxTestResult =ifelse(days_between_MDDx <60, "Positive MDDx", "No MDDx")) |>filter(!is.na(MDDxDate))# write.csv(PBMClaims_MDDxTest, "./data/output/PBMClaims_MDDxTest.csv")
The code says something lie this:
The SQL code retrieves data from the “shcavos” table in the “claims” database. It selects distinct records and specific columns including the primary ID, member ID, beginning date, primary diagnosis, and diagnostic codes. The query also checks for matches with specific diagnosis codes by joining with a temporary table called “#ValueSetListMy2023” that contains the desired codes. The resulting table includes an additional column indicating if a match was found for each record.
The R code performs the following operations:
Import the MDDxClaims data from a CSV file.
Rename the “primaryID” column to “PrimaryId” in the MDDxClaims data.
Add a new column “ProjectId_MDD” to the MDDxClaims data with random 10-digit numbers.
Filter out rows with missing “begDate” values from the MDDxClaims data and rename the “begDate” column to “MDDxDate”.
Select specific columns from the MDDxClaims data.
Filter the PBMClaims_NMHTest data to keep only rows where “NMHTestResult” is “New Start” and rename the “PrimaryId.x” column to “PrimaryId”.
Merge the filtered PBMClaims_NMHTest and MDDxClaimsTest data on the “PrimaryId” column, keeping all rows and columns.
Convert the “IPSDate” and “MDDxDate” columns to Date format.
Calculate the number of days between “IPSDate” and “MDDxDate” as an absolute value.
Add a new column “MDDxTestResult” based on the condition: if the number of days is less than 60, the value is “Positive MDDx”; otherwise, it is “No MDDx”.
Filter out rows with missing “MDDxDate” values from the merged data.
Overall, the code retrieves MDDx claims data, performs data transformations and comparisons, and filters the data based on specific conditions (ChatGPT, personal communication, July, 2023).
Fantastico!
Wow! I can’t believe that we actually pulled that off!
The Test for Negative Medication History determines if an individual is a new recipient of an antidepressant medication. An individual passes this test if they have not had a prescription for an antidepressant medication within 105 days prior to the IPSD.
Be patient. If you are replicating this project, this part of the program takes a minute to run. If you think about what it is doing, it is comparing every row of claims (~147,000 rows) against the IPSD Intake Period range, and for all of the positive returns, comparing each one to each of the the original 147,000 rows for the NMH test.
Think about trying to guess the first 2 numbers on the bike lock, multiplied by 147,000. Anyway, it’s a lot. Go grab some coffee.
In order to conduct the test for Negative Medication History we took the following steps:
PBM Claims dataset is queried from the Health Choice data warehouse. This initial pull includes the entire potential range of PBM Claims for the Intake Period and the Negative Medication History period. Refer again to the visualization above for a visual aid.
Conduct the Negative Medication History Test
If the fill date is within the eligible Intake Period (IPSD), and
If there are no other fills within 105 days before the fill date, then
The test confirms the Negative Medication History (TRUE), if not
The test denies the Negative Medication History (FALSE)
Be patient. This part of the program takes about 20 minutes to run. If you think about what it is doing, it is comparing every row (147,000 rows) against the IPSD range, and for all the positive returns comparing each one to each of the the original 147,000 rows for the NMH test. Think about trying to guess the first 2 numbers on the bike lock. Anyway, it’s a lot. Go grab some coffee.
Negative Medication History Test
Code
# Read the data from the IPSD test resultsclaims <- PBMClaims_MDDxTest# Step 1: Select the desired columns and add ProjectId columnselected_columns <- claims %>%select(clientID, PrimaryId, MemberID, genericnme, GpiNumber, ProjectId_IPSD, IPSDate, IPSDTestResult, ProjectId_MDD, MDDxDate, PrimaryDiagnosis, days_between_MDDx, MDDxTestResult ) # Step 2: Create copies of the tableoriginal <- selected_columnstest <- selected_columns# Step 3: Join the two tables based on the given conditionsjoined_table <- original %>%#mutate(IPSDTestResult = as.logical(IPSDTestResult)) %>%filter(MDDxTestResult =="Positive MDDx") %>%inner_join(test, by =c("clientID", "genericnme"))# Select desired variablesjoined_table <- joined_table |>select(clientID, PrimaryId.x, MemberID.x, genericnme, GpiNumber.x, ProjectId_IPSD.x, IPSDate.x, IPSDTestResult.x, ProjectId_MDD.x, MDDxDate.x, PrimaryDiagnosis.x, days_between_MDDx.x, MDDxTestResult.x, ProjectId_IPSD.y, IPSDate.y ) # Step 4: rename dtefilled.x and dtefilled.yjoined_table <- joined_table %>%rename(NMHTestDate = IPSDate.y)# Step 4 Rogue: format as datejoined_table <- joined_table %>%mutate(IPSDate.x =as.Date(IPSDate.x),NMHTestDate =as.Date(NMHTestDate))# Step 4 Rogue 1: Compare IPSD and NMH datesjoined_table <- joined_table %>%mutate(comparison_result_NMH =case_when( IPSDate.x < NMHTestDate ~"IPSD is prior to NMHTest date", NMHTestDate < IPSDate.x ~"IPSD is after test NMHTest date",TRUE~NA_character_ ))# Step 5: Determine the number of days between IPSDate and NMHTestDate as an absolute valuejoined_table <- joined_table %>%mutate(days_between_NMH =abs(as.numeric(IPSDate.x - NMHTestDate)))# Step 6: Determine "New Start" or "Positive Med History" based on days_betweenjoined_table <- joined_table %>%mutate(NMHTestResult =ifelse(days_between_NMH >105, "New Start", "Positive Med History"))# Do not write this, it is very large 25 million# write.csv(joined_table, "./data/output/PBMClaims_NMHTest.csv")PBMClaims_NMHTest <- joined_table
The code performs various operations on a dataset called “claims” (assumed to be in CSV format) related to pharmacy claims data with specific columns. Here’s a layman’s summary of the code:
Select desired columns from the claims data and add a new column called “ProjectId” with randomly generated 10-digit numbers.
Create copies of the table named “original” and “test” based on the selected columns.
Join the “original” and “test” tables based on specific conditions related to client ID and generic name.
Compare the “IPSDate” (date from the original table) with the “NMHTestDate” (date from the test table). Identify whether the IPSDate is before or after the NMHTestDate.
Calculate the number of days between the IPSDate and NMHTestDate as an absolute value.
Determine the “NMHTestResult” based on the number of days between IPSDate and NMHTestDate. If the number of days is greater than 105, the result is “New Start”; otherwise, it is “Positive Med History”.
Save the resulting joined table as a CSV file called “PBMClaims_NMHTest.csv”.
The overall purpose of the code seems to be to process and analyze pharmacy claims data, compare dates, and classify the results based on the number of days between certain dates (ChatGPT, personal communication, July, 2023).
Woo hoo! It worked!
We have now determined that there is:
A valid Index Prescription Start Date (IPSD)
A valid service for Major Depressive Disorder (MDDx)
Now that we have confirmed IPSD, NMH, and MDDx, we will search for the member information in our enrollment records. We then determine if there was an eligible enrollment period associated with the prescription fill.
An prescription fill is eligible if the individual had continuous enrollment for -105 days before the IPSD, AND +231 days after the IPSD.
Compare the Index Prescription Start Date against the member enrollment roster developed in the Alliance Progress Report 8. We use the full global members roster in this case because we are not filtering for active membership only. Individuals who are currently disenrolled are included in the dataset as well.
Test for Continuous Enrollment
Code
# Import the original global members roster that is created in the Alliance Progress ReportGlobalMembers_orig <-read_xlsx("./data/data_original_glblmbrs_2023-05-01_globalMembersRoster.xlsx", sheet ="Sheet1")# Create a working copy of the data and rename "x" to # "PrimaryId"GlobalMembers <- GlobalMembers_orig |>rename("PrimaryId.x"= x)# Filter to rule out cases without a "Positive MDDx"PBMClaims_CETest <- PBMClaims_NMHTest |>filter(NMHTestResult =="New Start")# Using the PBMClaims_MDDxTest table as a base, attach Global Members data by PrimaryIdPBMClaims_CETest1 <-merge(x = PBMClaims_CETest,y = GlobalMembers,by ="PrimaryId.x",all.x =TRUE) # Calculations for the prior enrollment period# Convert the columns to Date format if they are not already in Date formatPBMClaims_CETest1$IPSDate.x <-as.Date(PBMClaims_CETest1$IPSDate.x)PBMClaims_CETest1$bhhEffectiveDate <-as.Date(PBMClaims_CETest1$bhhEffectiveDate)# Calculate the number of days between IPSDate and bhhEffectiveDatePBMClaims_CETest1$days_between_CEPre <-as.numeric(PBMClaims_CETest1$IPSDate.x - PBMClaims_CETest1$bhhEffectiveDate)# Add a column "CEPreTest" based on the conditionPBMClaims_CETest1 <- PBMClaims_CETest1 %>%mutate(CEPreTest =ifelse(days_between_CEPre >=105, "Continuous Prior Enrollment", "No Continuous Prior Enrollment"))# Calculations for the post prescription enrollment period# Convert the columns to Date format if they are not already in Date formatPBMClaims_CETest1$IPSDate.x <-as.Date(PBMClaims_CETest1$IPSDate.x)PBMClaims_CETest1$disenrollmentDate <-as.Date(PBMClaims_CETest1$disenrollmentDate)# Calculate the number of days between IPSDate and bhhEffectiveDatePBMClaims_CETest1$days_between_CEPost <-as.numeric(PBMClaims_CETest1$disenrollmentDate - PBMClaims_CETest1$IPSDate.x)# Add a column "CEPostTest" based on the conditionsPBMClaims_CETest1 <- PBMClaims_CETest1 %>%mutate(CEPostTest =ifelse(days_between_CEPost >=231|is.na(disenrollmentDate), "Continuous Post Prescription Enrollment", "No Continuous Enrollment"))# Add a finnal test column "CETestResult" based on the conditionsPBMClaims_CETest1 <- PBMClaims_CETest1 %>%mutate(CETestResult =ifelse(CEPreTest =="Continuous Prior Enrollment"& CEPostTest =="Continuous Post Prescription Enrollment","Continuous Enrollment", "No Continuous Enrollment"))# Do not write, very large 7.9 million# write.csv(PBMClaims_CETest1, "./data/output/PBMClaims_CETest.csv")
The R code performs the following operations:
Imports the original global members roster data from an Excel file into a variable called GlobalMembers_orig.
Creates a working copy of the GlobalMembers_orig data and renames the column “x” to “PrimaryId”.
Combines the PBMClaims_MDDxTest table with the GlobalMembers data based on the “PrimaryId” column, storing the result in PBMClaims_CETest1.
Filters out cases in PBMClaims_CETest1 that do not have a “Positive MDDx” value in the “MDDxTestResult” column.
Converts the “IPSDate” and “bhhEffectiveDate” columns in PBMClaims_CETest1 to the Date format if they are not already.
Calculates the number of days between the “IPSDate” and “bhhEffectiveDate” columns and stores the result in the “days_between_CEPre” column.
Adds a new column called “CEPreTest” to PBMClaims_CETest1 based on the condition that if the “days_between_CEPre” is greater than or equal to 105, the value is “Continuous Prior Enrollment,” otherwise it is “No Continuous Prior Enrollment.”
Converts the “IPSDate” and “disenrollmentDate” columns in PBMClaims_CETest1 to the Date format if they are not already.
Calculates the number of days between the “IPSDate” and “disenrollmentDate” columns and stores the result in the “days_between_CEPost” column.
Adds a new column called “CEPostTest” to PBMClaims_CETest1 based on the conditions that if the “days_between_CEPost” is greater than or equal to 231 or if the “disenrollmentDate” is NA (missing), the value is “Continuous Post Prescription Enrollment,” otherwise it is “No Continuous Enrollment.”
Adds a final column called “CETestResult” to PBMClaims_CETest1 based on the conditions that if both “CEPreTest” is “Continuous Prior Enrollment” and “CEPostTest” is “Continuous Post Prescription Enrollment,” the value is “Continuous Enrollment,” otherwise it is “No Continuous Enrollment.”
Overall, the code identifies cases with continuous enrollment, performs calculations related to enrollment periods, and determines the final enrollment status based on the results of the prior and post-prescription enrollment tests. (ChatGPT, personal communication, July, 2023).
Eligibility Model for Antidepressant Medication Management, Check!
A valid service for Major Depressive Disorder, and
A valid period of enrollment.
The resulting dataset presents a list of members with indicators for each of the respective tests. A person with a prescription fill that met each of the conditions is included, and will be compared with the VBP Quality Roster Data.
Code
# Rule out cases without continuous enrollmentPBMClaims_AMMEligible <- PBMClaims_CETest1 |>filter(CETestResult =="Continuous Enrollment")# Select final variablesPBMClaims_AMMEligible <- PBMClaims_AMMEligible |>select("PrimaryId.x","clientID","MemberID.x","ahcccsId", "ProjectId_IPSD.x", "IPSDate.x", "genericnme", "ProjectId_MDD.x","MDDxDate.x","PrimaryDiagnosis.x","days_between_MDDx.x","MDDxTestResult.x","ProjectId_IPSD.y","NMHTestDate","days_between_NMH","NMHTestResult","bhhEffectiveDate","disenrollmentDate","days_between_CEPre","CEPreTest","days_between_CEPost","CEPostTest", "CETestResult","bhhShortname","sex","dob","zipCode","resCountyName")|>filter(bhhShortname %in%c("CPIH", "TGC", "SBH", "CBI", "WYGC", "SHG", "MMHC", "LCBHC"))# do not write, too big 3.9 million# write.csv(PBMClaims_AMMEligible, "./data/output/PBMClaims_AMMEligible.csv")
The R code performs the following operations:
Filters the PBMClaims_CETest1 data to keep only cases with “Continuous Enrollment” based on the “CETestResult” column.
Selects a subset of variables from the filtered data, including columns like “PrimaryId,” “MemberID,” “IPSDate,” “NMHTestDate,” and others.
Filters the data further to include only cases where the “bhhShortname” column matches specific values, such as “CPIH,” “TGC,” “SBH,” and others.
Overall, the code selects cases that meet the criteria of continuous enrollment and specific “bhhShortname” values and prepares a subset of variables for further analysis or export (ChatGPT, personal communication, July, 2023).
Value-based Purchasing Quality Reports (VBP QR)
Now that we have a reliable dataset for the Real Data (adjudicated claims), we will introduce the Report Data (VBP Quality Roster). We will import and aggregate the Report Data, clean it up, and then compare it with Real Data.
The VBP Roster comes to us from Health Choice, but the underlying data is analyzed and produced by a third party vendor, Cotivity.
The Alliance receives a separate report for each of the Alliance Providers. This procedure outlines how the VBP QR is input from the original report and transformed to actionable data.
The resulting table is a complete, cleaned roster of all of the individual member events, for all of the VBP Measures, for all of the Alliance Providers. It is then filtered to assess each measure individually.
This table is used to construct the VBP Quality Report Dashboard as well as the Alliance Progress Report
The most recent Value-based Purchasing (VBP) Quality Roster (04-27-2023) for each Alliance Provider (AP) was aggregated. The Roster page from the excel data model was extracted from each of the individual reports and compiled into one aggregate data frame that contains the results of all Alliance Providers.
The VBP Report data is cumulative over the VBP measurement year 01-01-2023 to 12-31-2023 and has a 60 day claims lag, such that the 04-27-2023 VBP QR contains claims adjudicated through 02-28-2023.
The SubMeasureID variable was filtered for this study to only include the AMM2 measure, and a list of the unduplicated Member IDs.
Import VBPQR Data
Code
# Import the unaltered VBP report, "Detail" sheet, as received from HCA# 5/1/23 sheet = "Detail" was change by HCA to sheet = "Roster"vbp_cbi <-read_xlsx("./data/VBPReports/Quality/vbpbhh_report_2023-05-31_94-2880847_Community_Bridges_HCA_BHH_VBP_Quality_Roster.xlsx", sheet ="Roster")## vbp_cbi <- vbp_cbi [,-1] ## colnames(vbp_cbi) <- c("BCBSAZ Health Choice" ,"...2", "...3", "...4", "...5", "...6", "...7", "...8", "...9")vbp_cpih <-read_xlsx("./data/VBPReports/Quality/vbpbhh_report_2023-05-31_86-0215065_Change_Point_Integrated_Health_HCA_BHH_VBP_Quality_Roster.xlsx", sheet ="Roster")vbp_lcbhc <-read_xlsx("./data/VBPReports/Quality/vbpbhh_report_2023-05-31_86-0250938_Little_Colorado_Behavioral_Health_HCA_BHH_VBP_Quality_Roster.xlsx", sheet ="Roster")vbp_mmhc <-read_xlsx("./data/VBPReports/Quality/vbpbhh_report_2023-05-31_86-0214457_Mohave_Mental_Health_HCA_BHH_VBP_Quality_Roster.xlsx", sheet ="Roster")vbp_ph <-read_xlsx("./data/VBPReports/Quality/vbpbhh_report_2023-05-31_86-0206928_Polara_Health_HCA_BHH_VBP_Quality_Roster.xlsx", sheet ="Roster")vbp_sbhs <-read_xlsx("./data/VBPReports/Quality/vbpbhh_report_2023-05-31_86-0290033_Southwest_Behavioral_Health_HCA_BHH_VBP_Quality_Roster.xlsx", sheet ="Roster")vbp_shg <-read_xlsx("./data/VBPReports/Quality/vbpbhh_report_2023-05-31_86-0207499_Spectrum_Health_Group_HCA_BHH_VBP_Quality_Roster.xlsx", sheet ="Roster")vbp_tgc <-read_xlsx("./data/VBPReports/Quality/vbpbhh_report_2023-05-31_86-0223720_The_Guidance_Center_HCA_BHH_VBP_Quality_Roster.xlsx", sheet ="Roster")# Pro Tip# if any of the tables pick up rogue columns...# vbp_cbi <- vbp_cbi [,-1] # colnames(vbp_cbi) <- c("BCBSAZ Health Choice" ,"...2", "...3", "...4", "...5", "...6", "...7", "...8", "...9")# Bind the Details sheet from all providers into one tableDataRaw_VBPQR_AllAPsCombined <-rbind( vbp_cbi, vbp_cpih, vbp_lcbhc, vbp_mmhc, vbp_ph, vbp_sbhs, vbp_shg, vbp_tgc)# write to csv# date of file = date of VBP QR report# write.csv(DataRaw_VBPQR_AllAPsCombined, "./data/output/2023-05-31_DataRaw_VBPQR_AllAPsCombined.csv")
Wrangle Said Data
Code
# create a safe copy of the original dataVBPQR_AllAPsCombined_Cleaned <- DataRaw_VBPQR_AllAPsCombined# Filter out superfluous rows of nonsense dataVBPQR_AllAPsCombined_Cleaned <- VBPQR_AllAPsCombined_Cleaned |>filter(`...2`!="NA")# Set column names to headers, which get imported on row 1 #5/2/23 - updated from "6" colnames(VBPQR_AllAPsCombined_Cleaned) <- VBPQR_AllAPsCombined_Cleaned [1,] # Remove the first row of data that headersVBPQR_AllAPsCombined_Cleaned <- VBPQR_AllAPsCombined_Cleaned[-1,]# 5/1/23 - Create SubMeasureIDVBPQR_AllAPsCombined_Cleaned$`SubMeasure ID`<-substr(VBPQR_AllAPsCombined_Cleaned$Measure, 1, 3)# write.csv(VBPQR_AllAPsCombined_Cleaned, "./data/output/VBPQualityRoster.csv")
Code
# create a duplicate at this phase to be used in later evaluationVBPQR_AllAPsCombined_Cleaned2 <- VBPQR_AllAPsCombined_Cleaned |>filter(`SubMeasure ID`=="AMM")write.csv(VBPQR_AllAPsCombined_Cleaned2, "./data/output/VBPQualityRoster_AMM.csv")# Isolate member ID for the validationVBPQR_AllAPsCombined_Cleaned <- VBPQR_AllAPsCombined_Cleaned |>filter(`SubMeasure ID`=="AMM") |>select(`Member ID`)# write.csv(VBPQR_AllAPsCombined_Cleaned, "./data/output/VBP_Validation.csv")
Import VBPQR data:
The code imports several VBP (Value-Based Purchasing) reports from different providers. These reports contain information about the quality of services related to behavioral health.
Each provider’s report is stored in a separate variable, such as vbp_cbi, vbp_cpih, vbp_lcbhc, etc. These variables represent the data tables within the reports.
The code reads the “Roster” sheet from each provider’s report file and stores the data in the corresponding variable.
All the provider data tables are then combined into one table called DataRaw_VBPQR_AllAPsCombined. This table contains the combined data from all providers.
Wrangle Said Data:
A copy of the combined data table, DataRaw_VBPQR_AllAPsCombined, is created for cleaning and manipulation.
Superfluous rows of irrelevant data are filtered out from the table.
The first row of the table, which contains headers, is used to set the column names for the table.
The first row of data (headers) is removed from the table.
A new column called “SubMeasure ID” is created by extracting the first three characters from the “Measure” column.
The cleaned and manipulated data table is saved as VBPQR_AllAPsCombined_Cleaned.
Wrangle VBPQR data 2:
Another copy of the cleaned data table, VBPQR_AllAPsCombined_Cleaned, is created for further evaluation.
This copy is filtered to include only rows where the “SubMeasure ID” is “AMM”.
The filtered data table is saved as VBPQR_AllAPsCombined_Cleaned2.
The original cleaned data table is further filtered to isolate the “Member ID” column for validation.
The filtered data table is saved as VBPQR_AllAPsCombined_Cleaned for validation purposes.
In summary, the code imports VBPQR reports from different providers, combines them into a single table, and performs data cleaning and manipulation to prepare the data for analysis and validation. The resulting data tables are saved for further use (ChatGPT, personal communication, July, 2023).
AMM Validation Data Modeling
The Validation Model for AMM is Conceptual and Logical. This model will help to define the systems surrounding measurement of AMM. This model also outlines a set of logical rules and structures of data to be used across agencies and measures.
This model extracts data from multiple VBP Quality Reports and aggregates it them into a single dataset. It also collects adjudicated claims data from the Health Choice data warehouse.
The 2 datasets are then joined to create the Validation Matrix, comparing VBP cases against adjudicated claims.
The Validation Matrix is the table that will be used for evaluation! Phew! :)
Report Data: VBP Quality Roster Reports
The VBPQR_AllAPsCombined_Cleaned data frame was loaded to the test model. The data was summarized, per member, by counting the instances of eligibility per member. This ultimately creates a vector of unduplicated Member IDs called VBP_Unduplicated.
Real Data: Adjudicated Claims
The AMMEligibilityTest claims data frame was loaded to the test model. It was then summarized by counting the instances of HEDIS My2022 eligible claims per member. This creates a vector of unduplicated Member IDs called AMMClaims_Unduplicated.
The Validation Matrix
The VBP_Unduplicated and the AMMClaims_Unduplicated are joined, and all rows of data from both variables are included, regardless of match. The resulting data frame is called Validation_Matrix.
This table contains all of the unduplicated VBPQR MemberIDs, and all of the unduplicated Claims MemberIDs.
The data is assessed for cases where a VBP MemberID is validated against a Claims MemberID. - A positive result is called “Match”, and a negative result is called “NoMatch”
The Validation_Matrix is the table that will be used for evaluation! Phew! :)
Code
# Extract just the member ID from the VBPQR AllAps, and than remove any duplicatesVBP_Unduplicated <- VBPQR_AllAPsCombined_Cleaned |>group_by(`Member ID`) |>rename("MemberID"=`Member ID`) |>count()# write.csv(VBP_Unduplicated, "./data/output/VBP_Unduplicated.csv")
Code
# Extract just the member ID from the PBMClaims_AMMEligible, and then remove any duplicatesAMMClaims_Unduplicated <- PBMClaims_AMMEligible |>rename(MemberID = MemberID.x) |>filter(MemberID !="NULL") |>group_by(MemberID) |>count()# write.csv(AMMClaims_Unduplicated, "./data/output/AMMClaims_Unduplicated.csv")
Code
# Combine the VBP unduplicated members with the AMMClaims unduplicated, keeping all values, and matching them on MemberID whenever possible.Validation_Matrix <-merge(x = VBP_Unduplicated,y = AMMClaims_Unduplicated,by ="MemberID",all =TRUE) |>rename("VBP"= n.x,"claims"= n.y) |>mutate(Match =if_else((is.na(VBP) |is.na(claims)), "NoMatch", "Match"))# write.csv(Validation_Matrix, "./data/output/ValidationMatrix.csv")
VBP Reports
8 VBP report details sheets were imported, merged and cleaned, creating DataRaw_VBPQR_AllAPsCombined with 4,424 observations of 9 variables.
VBPQR_AllAPsCombined_Cleaned was created from the master to isolate instances of FUH7, and then select for Member ID, ultimately yielding 1,691 observations of MemberIDs.
Claims
Pharmacy Records from HCA Pharmacy Benefit Manager were queried, including all claims for eligible AMM medications, for the date range including the IPSD and the NMH Tests. There were 196,520 records.
Behavioral Health records were querried from the HCA behavioral health claims system for the date range including all eligible dates for a Major Depressive Diagnosis. There were 265,060 records.
Enrollment
Enrollment data was queried from the HCA Global Members database for the entire range of possible enrollment and disenrollment dates. There were 367,940 members identified.
This R code is performing some data manipulation and analysis tasks. Let me break it down for you:
The first block of code, labeled “Modeling VBP Unduplicated,” is doing the following:
The dataset VBPQR_AllAPsCombined_Cleaned is being grouped by the column “Member ID.”
The column “Member ID” is then renamed to “MemberID.”
The number of occurrences of each unique “MemberID” is counted and stored in a new dataset called VBP_Unduplicated.
There is a commented out line (write.csv(…)) that suggests the intention to write the VBP_Unduplicated dataset to a CSV file.
The second block of code, labeled “Modeling Claims Unduplicated,” performs the following actions:
The dataset AMMEligibilityTest_AllEligible is filtered to exclude any rows where the “MemberID” is equal to “NULL.”
The dataset is then grouped by the “MemberID” column.
The number of occurrences of each unique “MemberID” is counted and stored in a new dataset called AMMClaims_Unduplicated.
There is a commented out line (write.csv(…)) that suggests the intention to write the AMMClaims_Unduplicated dataset to a CSV file.
The third block of code, labeled “Modeling Validation Matrix,” combines the previous two datasets (VBP_Unduplicated and AMMClaims_Unduplicated) to create a validation matrix:
The datasets VBP_Unduplicated and AMMClaims_Unduplicated are merged based on the common column “MemberID.” The resulting merged dataset is stored in Validation_Matrix.
The columns n.x and n.y (which represent the counts from the previous datasets) are renamed to “VBP” and “claims,” respectively.
A new column called “Match” is added to the Validation_Matrix dataset. The “Match” column is populated with the value “NoMatch” if either the “VBP” or “claims” values are missing (NA), and “Match” otherwise.
There is a commented out line (write.csv(…)) that suggests the intention to write the Validation_Matrix dataset to a CSV file.
In summary, this code takes two datasets (VBPQR_AllAPsCombined_Cleaned and AMMEligibilityTest_AllEligible), performs data manipulation and aggregation operations on them, and creates a validation matrix (Validation_Matrix) to determine matches and mismatches based on the “MemberID” column. The intention is to potentially write the resulting datasets to CSV files for further analysis or storage.
Results
Validating Report Data using Real Data
In order to conduct the validation test, all records from VBP_Unduplicated are included, while Claims_Unduplicated records are only included if they have a positive match with VBP_Unduplicated on the variable Member ID.
Validation Matrix Results
The Validation Matrix dataset is a list of all distinct member IDs in the context of claims and VBP reports.
Validation
The cases when a member identified on the Health Choice VBP Quality Roster is also identified in adjudicated claims were as follows:
Valid (“Match”): 266 of the 1,691 members on the VBP Quality Roster were also found among eligible Health Choice pharmacy claims.
Not-Valid (“No Match”): 1,425 of the 1,691 members on the VBP Quality Roster were not found among eligibile Health Choice pharmacy claims.
Compliance
The compliance status of cases on the Health Choice VBP Report were as follows.
Non-Compliant: 703 cases were not found to have the expected duration of antidepressant medication treatment.
Compliant: 988 cases were found to have the expected duration of antidepressant medication treatment.
Validation by Compliance:
The matrix that results from combining the validation status and the compliance status of a given member provides the following results. Compliance status is provided by the VBP Quality Roster. The Alliance has not created a model for the assessment of compliance, yet.
Matched, NonCompliant (MNC): 36
A member that is found in both the VBP Quality Roster AND found in Health Choice claims, who DID NOT receive the expected duration of antidepressant medication treatment
Matched, Compliant (MC): 230
A member that is found in both the VBP Quality Roster AND found in Health Choice claims, who DID indeed receive the expected duration of antidepressant medication treatment
NonMatched, NonCompliant (NMNC): 667
A member that is found in the VBP Quality Roster, BUT NOT found in Health Choice claims, who DID NOT receive the expected duration of antidepressant medication treatment
NonMatched, Compliant (NMC): 758
A member that is found in the VBP Quality Roster, BUT NOT found in Health Choice claims, who DID indeed receive the expected duration of antidepressant medication treatment
Analysis
The Pearson’s Chi-Square test can be used to confirm if there is a significant difference between the expected result and the observed result in this comparison.
Chi Square test was run at alpha = .05. There was a statistically significance between the groups (X^2 (1, N=1691) = 90.633, p<.001), indicating that the groups in the Validation Matrix are disproportionately impacted.
We also found that only about 16% of the cases that were identified by the VBP Quality roster were also found in adjudicated claims. In other words, only 16% of the VBP Quality Roster cases were valid.
Code
# Create a 2 x 2 matrix for the chi square test# transform for Chi Square testComp_ChiSq <- Compliance |>select(Match, Gap_Status) |>group_by(Match, Gap_Status) |>#mutate(Numerator = as.character(Numerator)) |> count() |>pivot_wider(names_from ="Gap_Status",values_from ="n")# mutate(`0` = as.double(`0`),# `1` = as.double(`1`))# write.csv(Comp_ChiSq, "./data/output/Comp_ChiSq.csv")chisq.test(Comp_ChiSq[-1], correct =FALSE)
# Create chi square column chartChiSquare_Barchart <- Compliance |>select(Match, Gap_Status) |>group_by(Match, Gap_Status) |>mutate(Numerator =if_else(Gap_Status =="OPEN", "NonCompliant", "Compliant")) |>count() |>ggplot(aes(fill=Match, x=Gap_Status, y=n)) +geom_col() +scale_fill_manual(values =c(Match ="#b60ef9", NoMatch ="#520096"),name ="Validation",labels =c("VBP and Claims", "VBP and No Claims")) +labs(title ="VBP Report Member Event Validation",subtitle ="VBP Quality Roster validation against adjudicated claims",caption ="*From claims adjudicated through April 27, 2023") +ylab("Number of Distinct Members") +xlab("Compliance to expected AMM treatment duration (115 days)") +scale_x_discrete(labels =c("115 days Tx (Compliant)", " Less than 115 days (Non-Compliant)")) +theme(axis.title.y =element_text(vjust =2),plot.subtitle =element_text(face ="italic"),plot.caption =element_text(face ="bold.italic", hjust =0, vjust =-1) ) +annotate("text", x =1, y =800, label ="p < .001", color ="white", fontface ="bold")# Save the plot as a PNG file with a transparent backgroundggsave("./data/output/chisquared_transparentbackground.png", width =7, height =5, dpi =300, bg ="transparent")# ggsave("./data/output/AMMchisquare_barchart.png", width = 7, height = 5, dpi = 300)# ggsave("C:/Users/RyanK/OneDrive - The NARBHA Institute/R_Studio/progressReport/images/20230630_AMMchisquare_barchart.png", width = 7, height = 5, dpi = 300)ChiSquare_Barchart
Discussion
Eligibility Model
The process of determining eligible cases for the AMM measurement is very complex. We hope that we were able to adequately describe the process and the nature of the resulting dataset.
After determining the eligible cases, the process of validating that list of members against the VBP Quality Roster is fairly straight forward, and followed an almost identical process to the FUH7 Validation.
The evaluation of the aggregated Alliance Provider VBP Quality Rosters identified 1,691 eligible members. However, only 16% of those (266 members) could be validated by matching the VBP QR record with an eligible claim.
Conclusions
Our goal is accuracy and transparency. With this in mind, The Alliance should keep a close eye on this measure. With the 1st quarter of data for the 2023 measurement year in the books, it is a concern that there are so few valid cases in the VBP Quality Report. This data will be reported to Health Choice leadership at our quarterly joint operating meeting.
The difference between the validation groups was statistically significant. It is also easily visualized. The implications of this data are considerable. The current AMM performance score for the Alliance is 58.43%, or +1.14 percentage points above the mean. When that score is adjusted to only include cases that can be validated by matching with an eligible claim (the light purple), that performance scores rises to 86.5%, or +30.93 percentage points.
This is significant impact on the performance measurement of Alliance Providers. We recommend further investigation into the data and processes used by Health Choice. In the meantime, it is also recommended to research provider tools and innovations to improve the overall performance of the network while we continue to assess data validation.
Because HCA categorizes their PBM claims in terms of Generic Product Identifier (GPI), but NCQA does theirs in terms of National Drug Code (NDC), we have to crosswalk the eligible AMM medications from NDC to their corresponding GPI codes. Fortunately, it’s totally not a pain in the neck to backwards engineer this at all, lol.
The HEDIS My2023 Medication List Names identifies the eligible medications by providing the respective NDC numbers10. The AHCCCS Preferred Drug List is used to match NDC numbers to GPI numbers based on medication name11.
In order to create a cross walk that ties the NDC codes from NCQA to the GPI codes from Health Choice, we took the following steps:
Import the My2023 Medication to Code data from the My2023 Medication List Directory12
Filter the data to only include Antidepressant Medications
Rename some columns to provide consistency across tables
Import the AHCCCS Preferred Drug List 13. A side by side list of NDC and GPI codes is incredibly difficult to come by, and so we are using this list from 2019. Fortunately NDC and GPI are very stable.
Rename some columns to provide consistency across tables.
Filter Therapeutic Class for Antidepressant Other, Antidepressant SSRI
Merge the NCQA dataset with the AHCCCS dataset by matching on the the NDC variable.
Code
# Covert NDC to GPI#import the My2023 Medications to Code datasetMed_To_NDC <-read.csv("./data/My2023MedicationToCode.csv")Med_To_NDC_AMM <- Med_To_NDC |>filter(Medication.List.Name =="Antidepressant Medications") |>select( Medication.List.Name, Code, Generic.Product.Name )colnames(Med_To_NDC_AMM)[colnames(Med_To_NDC_AMM) =="Code"] <-"NDC_NationalDrugCode"colnames(Med_To_NDC_AMM)[colnames(Med_To_NDC_AMM) =="Medication.List.Name"] <-"MedicationList"colnames(Med_To_NDC_AMM)[colnames(Med_To_NDC_AMM) =="Generic.Product.Name"] <-"GenericProductName"# Import AHCCCS Preferred drug listNDC_To_GPI <-read.csv("./data/AHCCCS_PreferredDrugListChangesFor_08012019.csv")colnames(NDC_To_GPI)[colnames(NDC_To_GPI) =="Therapeutic.Class...Market.Basket"] <-"TherepeuticClass"colnames(NDC_To_GPI)[colnames(NDC_To_GPI) =="National.Drug.Code..NDC..MediSpan"] <-"NDC_NationalDrugCode"colnames(NDC_To_GPI)[colnames(NDC_To_GPI) =="MediSpan.Generic.Product.Indicator..GPI."] <-"GPI_GenericProductIdentifier"NDC_To_GPI_AMM <- NDC_To_GPI |>filter(c(TherepeuticClass =="ANTIDEPRESSANTS, OTHER"| TherepeuticClass =="ANTIDEPRESSANTS, SSRIs")) |>select(NDC_NationalDrugCode, GPI_GenericProductIdentifier, TherepeuticClass)# merge then filter for only AHCCCS preferred medications# So these are all of the ones from NCQA that AHCCCS has on thier list. And now we have our crosswalk to address claims. My2023NDCtoGPICrosswalk <-merge(x = Med_To_NDC_AMM,y = NDC_To_GPI_AMM,by ="NDC_NationalDrugCode",all.y =TRUE) |>select( MedicationList, TherepeuticClass, NDC_NationalDrugCode, GPI_GenericProductIdentifier, GenericProductName ) |>na.omit()write.csv(My2023NDCtoGPICrosswalk, "./data/output/My2023NDCtoGPICrosswalk.csv")concatenated_values_GPI <-paste0("'", My2023NDCtoGPICrosswalk$GPI_GenericProductIdentifier, "'", collapse =", ")
The Validation Matrix and the original dataset for PBM Claims are combined to form a member list with an indicator for VBP, claims, validation, and the associated PBM claims variables.
The Validation_Matrix was transformed and rejoined with AMMEligibilityTest to create a table that will be used to generate Member Follow Up Lists. This new table is called MemberFollowUpList.
Code
# Using the copy of VBPQR_AllAPsCombined_Cleaned we made above, rename MemberID to matchPBMClaims_AMMEligible <- PBMClaims_AMMEligible |>rename(MemberID = MemberID.x)# Rejoin the matched and non-matched MemberIDs to their claims dataMemberFollowUpList <-merge(x = Validation_Matrix,y = PBMClaims_AMMEligible,by ="MemberID",all =TRUE) |>drop_na(Match)# Do not write, too big, 3.9 million# write.csv(MemberFollowUpList,"./data/output/AMM_MemberFollowUpList.csv")
Some exploratory analyses were conducted on the resulting data from this model. The results are included here for further reference, and we invite any innovations that spring from its review.
Code
# Summarize top ten label namesMemberFollowUpList |>filter(!is.na(genericnme)) |>group_by(genericnme) |>summarise(percent =n() /nrow(MemberFollowUpList) *100) |>arrange(desc(percent)) |>head(10) |>kable()
genericnme
percent
SERTRALINE HCL TAB 100 MG
17.069079
VENLAFAXINE HCL CAP ER 24HR 75
10.968159
VENLAFAXINE HCL CAP ER 24HR 15
10.112130
FLUOXETINE HCL CAP 40 MG
10.091155
BUPROPION HCL TAB 100 MG
9.114629
TRAZODONE HCL TAB 50 MG
6.407451
TRAZODONE HCL TAB 100 MG
6.188917
FLUOXETINE HCL CAP 20 MG
4.887267
TRAZODONE HCL TAB 150 MG
4.854844
BUPROPION HCL TAB ER 24HR 300
4.430453
Code
# Visualize top 10 label namesMemberFollowUpList |>filter(!is.na(genericnme)) |>group_by(genericnme) |>summarise(percent =n() /nrow(MemberFollowUpList) *100) |>filter(percent >=1) |>arrange(genericnme) |>ggplot(aes(x = percent, y =reorder(genericnme, percent))) +geom_bar(stat ="identity", fill ="steelblue") +ylab(NULL) +xlab("Percentage of Total Prescription Fills") +ggtitle("Horizontal Bar Chart of Label Names with at least 1%") +theme_minimal() +theme(axis.text.y =element_text(angle =0, hjust =1, margin =margin(r =5)),axis.title.y =element_blank(),plot.title =element_text(hjust =1.0),axis.text.x =element_text(hjust =0),axis.text.y.right =element_text(hjust =0))
Code
# Evaluate date of serviceMemberFollowUpList |>mutate(IPSDate.x =as.Date(IPSDate.x)) |>mutate(month =floor_date(IPSDate.x, "month")) |>filter(month >=as.Date("2022-04-01") & month <=as.Date("2023-03-31")) |>count(month) |>ggplot(aes(x = month, y = n)) +geom_line() +geom_vline(xintercept =as.Date("2022-10-01"), linetype ="dashed", color ="#520096") +annotate("text", x =as.Date("2022-10-01"), y =max(MemberFollowUpList$n), label ="End RBHA Contract", vjust =-10.0, hjust =-0.025, color ="#520096") +scale_x_date(date_labels ="%b %Y", date_breaks ="1 month") +scale_y_continuous(labels = comma) +ylab("Number of Fills") +xlab(NULL) +ggtitle("Frequency of Antidepressant Medication Fills over Time") +theme_minimal() +theme(axis.text.x =element_text(angle =45, hjust =1))
Record of the R Studio Environment, files and objects
Code
# Install 'knitr' package if not already installedif (!require(knitr)) {install.packages("knitr")}
Loading required package: knitr
Warning: package 'knitr' was built under R version 4.2.2
Code
library(knitr)# Get a list of all objects in the environmentobjects <-ls()# Initialize an empty list to store object detailsobject_details <-list()# Iterate over each objectfor (object_name in objects) {# Get the object object <-get(object_name)# Get the number of observations and variables num_observations <-if (is.data.frame(object)) nrow(object) elseNA num_variables <-if (is.data.frame(object)) ncol(object) elseNA# Check if observations are NAif (!is.na(num_observations)) {# Create a data frame with object details object_info <-data.frame(Object = object_name,Observations = num_observations,Variables = num_variables)# Add the object details to the list object_details[[object_name]] <- object_info }}# Combine all object details into a single data frameall_object_details <-do.call(rbind, object_details)# Format the table using kableformatted_table <-kable(all_object_details, format ="markdown", align ="c")# Print the formatted tableprint(formatted_table)
Health Choice Arizona. (2023). Value Based Purchasing Quality Roster↩︎
Kivela, J.R. (2023). Value Based Purchasing Data Validation Model. The Northern Arizona Regional Behavioral Health Alliance.↩︎
NCQA. (2023). HEDIS and Performance Measurement. https://www.ncqa.org/hedis/↩︎
NCQA. (2023). HEDIS Measures and Technical Resources. https://www.ncqa.org/hedis/measures/↩︎
Kivela, J.R. (2023). Value Based Purchasing Data Validation Model. The Northern Arizona Regional Behavioral Health Alliance.↩︎
CRISP-DM help overview. (August 17, 2021). https://www.ibm.com/docs/en/spss-modeler/saas?topic=dm-crisp-help-overview.↩︎
Kivela, J.R. (June, 2023). Value Based Purchasing Report. The Northern Arizona Regional Behavioral Health Alliance.↩︎
Kivela, J.R., McMillian, J., Tewa, V. (June 2023). The Alliance Progress Report. The Northern Arizona Regional Behavioral Health Alliance.↩︎
Kivela, J.R. (June, 2023). Value Based Purchasing Report. The Northern Arizona Regional Behavioral Health Alliance.↩︎
NCQA. (2023). HEDIS Measures and Technical Resources. https://www.ncqa.org/hedis/measures/↩︎
Arizona Health Care Cost Containment System.AHCCCS Drug List (Effective April 1, 2023). https://www.azahcccs.gov/Resources/Downloads/PharmacyUpdates/AHCCCS_DRUG_LIST_04012023.pdf↩︎
NCQA. (2023). HEDIS Measures and Technical Resources. https://www.ncqa.org/hedis/measures/↩︎
Arizona Health Care Cost Containment System. AHCCCS Drug List (Effective April 1, 2023). https://www.azahcccs.gov/Resources/Downloads/PharmacyUpdates/AHCCCS_DRUG_LIST_04012023.pdf↩︎